Codebook generation system and associated methods

ABSTRACT

A codebook generation system and associated methods are generally described herein.

RELATED APPLICATIONS

This application is a continuation-in-part application of application Ser. No. 11/036,906 of the same title, inventor and commonly assigned to Intel, Corporation.

TECHNICAL FIELD

Embodiments of the invention are generally directed to communication systems and, more particularly, to a codebook generation system and associated methods.

BACKGROUND

Closed loop multiple-input-multiple-output (MIMO) systems typically transmit channel state information from a receiver to a transmitter. Transmitting the channel state information consumes bandwidth that might otherwise be available for data traffic.

Illustratively, conventional frequency division duplex (FDD) systems that employ beamforming (or, closed loop multiple input, multiple output (MIMO), the beamforming matrix (referred to herein as a codeword) generated in response to perceived channel conditions is computed and quantized at the receiver first, and then is provided to the source transmitter (e.g., via feedback). A conventional approach to reduce the overhead associated with this feedback is to provide matrix codebook(s) at each of the transmitter and the receiver, each of the codebook(s) comprising a plurality, or set, of potential beamforming matrixes that may be used depending on the channel conditions perceived at the receiver. When the receiver has identified the appropriate matrix codebook(s), the receiver will typically feed back only an index (instead of the actual matrix entries) that points to the appropriate codeword in the codebook(s) stored at the transmitter.

Thus, for a different combination of transmit antenna(e) (N_(t)) and data streams (N_(s)), a different matrix codebook is required. Conventionally, the size of the codebook is based on the number of transmit antennae and the number of data streams: N_(t)×N_(s). For some systems, e.g., one implementing the developing 802.16e¹, N_(t) and N_(s) are currently less than five (5) but are likely to increase to eight (8). Therefore, a substantial number of N_(t) by N_(s) combinations are anticipated, requiring a significant amount of memory within mobile communication devices in order to store such a large number of codebooks. ¹ See, e.g., the ANSI/IEEE Std 802.16-2001 Standard for Local and Metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems, its progeny and supplements thereto (e.g., 802.16a, .16d, and .16e).

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements and in which:

FIG. 1 is a block diagram of an example communication system within which embodiments of the invention may be practiced;

FIG. 2 is a flow chart of an example method for generating codebook(s), according to one embodiment;

FIGS. 3 provides a graphical representations of the performance of embodiments of the invention versus a conventional techniques;

FIG. 4 is a block diagram of an example communications device incorporating one or more embodiments of the invention; and

FIG. 5 is a block diagram of an example article of manufacture including content which, when executed by an accessing machine, causes the machine to implement one or more aspects of embodiment(s) of the invention.

DETAILED DESCRIPTION

Embodiments of a codebook generation system and associated methods are generally presented. According to one embodiment, described more fully below, a codebook generation agent (CGA) is presented which may implement a method for generating one or more matrix codebooks from vector codebooks.

According to one embodiment, the CGA is implemented in mobile devices (e.g., stations, subscriber units, handsets, laptops, etc.), although the invention is not limited in this regard. As developed more fully below, the CGA may develop one or more matrix codebook(s) from matrix codewords that are dynamically generated from vector codebook(s) for 2-, 3-, 4-, . . . , N-unit vectors already resident on the device in support of other features (e.g., single data stream beamforming). In this regard, the use of the vector codebook(s) for 2-, 3- and 4-unit vectors does not add any extra complexity or memory drain to the mobile device. On the contrary, by dynamically generating the matrix codebooks rather than having them stored on the mobile device, enables the mobile device to utilize the memory normally consumed by the matrix codebooks in support of other features and/or services.

More particularly, as developed more fully below, the CGA may implement one or more of four (4) disclosed techniques for generating the matrix codebooks. According to some embodiments, the codebook generation agent may leverage the Householder reflection and an appropriate one or more vector codebook(s) of 2-, 3- and/or 4-unit vector to generate one or more suitable matrix codeword(s) for compilation into a matrix codebook for a given set of channel conditions.

Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

Technical detail regarding some of the operating characteristics of the mobile devices and/or the wireless communication network(s) in which the CGA may be implemented may be found in, e.g., the IEEE 802.11, 1999 Edition; Information Technology Telecommunications and Information Exchange Between Systems—Local and Metropolitan Area Networks—Specific Requirements, Part 11: WLAN Medium Access Control (MAC) and Physical (PHY) Layer Specifications, its progeny and supplements thereto (e.g., 802.11a, .11g and. 11n). See, also, the IEEE Std 802.16-2001 IEEE Std. 802.16-2001 IEEE Standard for Local and Metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems, its progeny and supplements thereto (e.g., 802.16a, .16d, and .16e).

Example Communications Environment

In FIG. 1, a block diagram of an example wireless communication environment 100 is depicted within which embodiments of the invention may well be practiced. In accordance with the illustrated example embodiment of FIG. 1, an example communications environment 100 is depicted comprising one wireless communications device 102 in communication with another wireless communications device 106 through a wireless communication link 104. As used herein, communication environment 100 is intended to represent any of a wide range of wireless communication networks including, but not limited to, a near-field communication (NFC) network, a wireless local area network (WLAN), a wireless metropolitan area network (WMAN), a cellular radiotelephony network, a personal communication system (PCS) network, and the like.

According to one embodiment, communication network 100 is an 802.16x communication network, and device 102 is a base station while device 106 is a subscriber station, although the scope of the invention is not limited in this regard. In a closed-loop MIMO (or, as above, a beamforming system) the data signal is first weighted by a beamforming matrix V, and then selectively transmitted by a plurality of antennae, as shown. According to one embodiment, the data signal may comprise a number of data streams (N₁ . . . N_(s)), although the invention is not limited in this regard. The number of data streams may represent the number of spatial channels, with appropriate bit-loading, power weighting and subcarrier assignments, although the invention is not limited in this regard.

According to one embodiment, with four (4) transmit antennae and three (3) data streams (for ease of illustration), the transmitted signal (x) transmitted via the N_(t) antennae may be represented as: $\begin{matrix} {{x = {V \times s}}{where}{{V = \begin{bmatrix} v_{11} & v_{12} & v_{13} \\ v_{21} & v_{22} & v_{23} \\ v_{31} & v_{32} & v_{33} \\ v_{41} & v_{42} & v_{43} \end{bmatrix}},{{{and}\quad s} = \begin{bmatrix} s_{3} \\ s_{2} \\ s_{3} \end{bmatrix}}}} & (1) \end{matrix}$ As shown, s is an N_(s)-vector of data symbols, and V is the N_(t) by N_(s) beamforming matrix developed from information (e.g., matrix codebook(s) and or indices thereto) fed back from a remote receiver. According to one embodiment, the beamforming matrix V is typically unitary, and power/bit loading is applied on vector s, as introduced above.

Device 106 is depicted comprising a codebook generation agent (CGA) 108 to dynamically generate one or more matrix codebook(s) from which channel state information may; be characterized and fed back to the base station, 102. As introduced above, rather than storing one or more matrix codebooks, CGA 108 compiles the matrix codebooks necessary to characterize the channel state information from matrix codeword(s) dynamically generated from one or more vector codebook(s) for 2-, 3-, 4-, . . . , N-unit vectors. As discussed more fully below, the vector codebook(s) are recursively applied to a suitable transform (e.g., a Householder reflection) from the lowest-order codebook to the highest order codebook, as necessary to generate the desired size matrix codeword(s) from which the matrix codebook(s) are assembled.

It will be appreciated that but for the introduction of the CGA 108, device 106 is intended to represent any of a wide variety of electronic device(s) with wireless communication capability. In some embodiments, CGA 108 may well be implemented within a receiver element of a device. In other embodiments, CGA 108 is responsive to a communicatively coupled receiver to perform the functions described herein. According to some embodiments, CGA 108 may well be implemented in hardware, software, firmware and/or any combination thereof.

Example Operation

As introduced above, CGA 108 may generate the matrix codebook(s) from the one or more vector codebooks according to a number of techniques, each described more fully below. The first technique disclosed offers the closest approximation to the conventional technique of using stored matrix codebooks. The second through fourth technique disclosed also offer very good results, although with decreased computational complexity. In either case, the computational complexity is more than offset by the reduced memory that need be allocated to the storage of the matrix codebooks.

Turning to FIG. 2, a flow chart of an example method for dynamically generating one or more matrix codebook(s) is generally presented, according to one embodiment. As shown, the method begins with block 202 by dynamically identifying the size of the matrix codeword(s) required. More particularly, according to one embodiment CGA 108 disposed within, or otherwise responsive to a receiver (e.g., 106) may be invoked to determine the size of the matrix codebook necessary. According to one embodiment, the size of the codeword required is dependent upon the number of transmit antennae (N_(t)) and/or the number of spatial data streams (N_(s)) utilized in the communication channel, although other parameters may be considered as a supplement to, or in place of, N_(t) and/or N_(s). According to one embodiment, the necessary parameters are either supplied to, or perceived by the receiver and/or CGA 108 for use in determining the size of the matrix codeword to generate.

As shown, CGA 108 is depicted comprising vector codebooks for 2-, 3-, 4-, . . . , N-unit (or, parameter) vectors. Accordingly, CGA 108 dynamically selects the vector codebook(s) suitable for a particular element of the recursive process for generating an element of the matrix codeword(s), as provided more fully below.

In response to determining the necessary size of the matrix codeword, CGA 108 may dynamically select an appropriate one or more vector codebook(s) suitable for generating at least an element of the matrix codeword, block 204. According to one embodiment, the vector codebook(s) selected by CGA 108 may depend on which of the techniques will be employed to generate the matrix codeword(s). According to one embodiment, the technique to be used is dynamically selected by CGA 108 and may depend on any of a number of factors including, but not limited to, the current processing load of the receiver and/or CGA 108, the perceived quality of the channel, and the like. That is, the current processing load of the receiver and/or CGA 108 may be such that a lower complexity codebook generation technique is required. Similarly, if the perceived quality of the channel (e.g., through signal-to-noise ratio, received power level, etc.) is high, CGA 108 may determine that a lower complexity codebook generation technique will provide suitable results, whereas a poorer channel may benefit from use of a more complex technique that more closely approximates the use of conventional (stored) codebooks.

Once the matrix codebook is generated, conventional techniques for computation and quantization of the proposed beamforming matrix may be employed, such as the one described in co-pending U.S. patent application Ser. No. 10/937,097 entitled Recursive Reduction of Channel State Feedback by Li, et al., commonly assigned to the Assignee of this application, and incorporated by reference herein for all purposes.

Returning to block 206, as provided above CGA 108 may employ one or more of at least four (4) techniques for recursively generating one or more matrix codeword(s) from vector codebook(s) for 2-, 3-, 4-, . . . N-unit vectors. It will be appreciated that other techniques for generating a matrix codeword from vector codebooks may well be used without deviating from the scope and spirit of the claims, below. Each of the four techniques are presented, as follows.

Technique 1

According to one embodiment, CGA 108 may generate a matrix codeword, column by column, using vector codebooks starting from the smallest dimension of the matrix codeword and working towards the largest dimension. For example, to generate a 4×3 matrix codeword, CGA 108 may employ unit vectors of dimensions 2, 3, and 4 sequentially, starting from the most inner-most parentheses (or, lowest dimension) and working out towards higher dimensions of the codeword, as shown: ${V\quad\left( {l_{N_{t}},\ldots\quad,l_{N_{t} - N_{s} + 1}} \right)} = \quad{\quad{P_{N_{t}}{\quad{{\begin{matrix} \begin{bmatrix} 1 & {\quad{0\quad\cdots\quad 0}} \\ \begin{matrix} \begin{matrix} 0 & \quad \\ \vdots & P_{N_{t} - 1} \end{matrix} \\ \quad \\ {0\quad} \end{matrix} & \begin{bmatrix} 1 & ⋰ & {\quad 0} \\ \begin{matrix} 0 \\ \vdots \\ 0 \end{matrix} & P_{N_{t} - N_{s} + 2} & \begin{bmatrix} 1 & 0 \\ \begin{matrix} 0 \\ \vdots \\ 0 \end{matrix} & {v_{N_{t} - N_{s} + 1}\left( l_{N_{t} - N_{s} + 1} \right)} \end{bmatrix} \end{bmatrix} \end{bmatrix} \end{matrix}P_{i}} = {{I - {\frac{2}{{w^{H}w}}{ww}^{H}w}} = {{{\mathbb{e}}^{- {j\phi}_{i,1}}{v_{i}\left( l_{i} \right)}} - e_{1}}}}}}}$ In the special case, v_(i)=e₁, the householder reflection can be computed as. P_(i)=I. Without unnecessary repetition, it is understood that this special treatment is implied in all the following description.

The N_(t) by N_(s) matrix codeword is constructed from the last column recursively, where the iteration starts from the lower, right hand side corner. Successive iteration(s) adds one column and one row to the constructing matrix codeword. In this regard, let {v_(i)(l_(i))}_(l) _(i=1) ^(L) _(i) denote the codebook of i-dimension unit vectors with L_(i) codewords (i.e. vectors), where l_(i) is the codeword index. Let {v_(t)(l₁)}_(l) _(t=1) ^(L) _(i) =}1}, i.e. v₁(1)=1 and L₁=1. Let {V_(N) _(t) _(×N) _(s) (l)}_(l=1) ^(L) denote the matrix codebook of dimension N_(t) by N_(s) with L codewords, where $L = {\prod\limits_{i = 1}^{N_{s}}\quad{L_{i}.}}$

The matrix codebook {V_(N) _(t) _(×N) _(s) (l)}_(l=1) ^(L) can be constructed by N_(s) vector codebooks according to the following pseudo-code: I.  FOR  l_(N_(t) − N_(s) + 1) = 1 : L_(N_(t) − N_(s) + 1) I.1. If N_(t) > N_(s) I.1.1.  V_(t) = v_(N_(t) − N_(s) + 1)(l_(N_(t) − N_(s) + 1)) I.2. ELSE I.2.1.   V_(t) = 1 I.3. END II.  FOR    l_(N_(t) − N_(s) + 2) = 1 : L_(N_(t) − N_(s) + 2) II.1.  w = e^(−j  ϕ_(l))v_(N_(t) − N_(s) + 2)(l_(N_(t) − N_(s) + 2)) − e₁, $\begin{matrix} {{where}\quad\phi_{1}\quad{is}\quad{the}\quad{phase}\quad{of}\quad{the}\quad{first}} \\ {{entry}\quad{of}\quad{v_{N_{t} - N_{s} + 2}\left( l_{N_{t} - N_{s} + 2} \right)}\quad{and}} \end{matrix}\quad$ e₁ = [1,0 . . . 0]^(T). ${{II}{{.2}.\quad P_{N_{t} - N_{s} + 2}}} = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}$ ${{II}{{.3}.\quad V_{t}}} = {P_{N_{t} - N_{s} + 2}\begin{bmatrix} 1 & 0 \\ 0 & V_{t} \end{bmatrix}}$ III.  FOR  l_(N_(t) − N_(s) + 3) = 1 : L_(N_(t) − N_(s) + 3) . . .     . . . N_(s).  FOR  l_(N_(t)) = 1 : L_(N_(t)) N_(s).1.  w = e^(−j  ϕ_(l))v_(N_(t))(l_(N_(t))) − e₁, where  ϕ₁  is  the  phase  of  the  first  entry  of  v_(N_(t))(l_(N_(t))). ${N_{s}{{.2}.\quad P_{N_{t}}}} = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}$ ${{N_{s}{{.3}.\quad{V(l)}}} = {P_{N_{t}}\quad\begin{bmatrix} 1 & 0 & \cdots & 0 \\ 0 & \quad & \quad & \quad \\ \vdots & \quad & V_{t} & \quad \\ 0 & \quad & \quad & \quad \end{bmatrix}}},$ ${{where}\quad l} = {l_{N_{t}} + {\sum\limits_{i = {N_{t} - N_{s} + 1}}^{N_{t} - 1}{l_{i}{\prod\limits_{j = {i + 1}}^{N_{t}}\quad L_{j}}}}}$ N_(s).4.   END . . .     . . . II.4. END I.4. END

As shown above, CGA 108 may generate a matrix from the most inner core with the smallest dimension (the lowest dimension) to the full matrix. The lowest dimension core is either 1, or a vector of size N_(t)−N_(s)+1. Each expansion, or recursive iteration, effectively increases the size of the matrix by one row and one column. There are Ns FOR loops for Nt>Ns, and there are Nt−1 FOR loops for Nt=Ns. Each FOR loop corresponds to one expansion of the matrix codeword, each expansion generally comprising:

1) picking an appropriate vector from the vector codebook; 2) removing the phase of the first element of the vector by e^(−jφ) _(l) v_(N) _(t) (l_(N) _(t) ) and subtract one from the first element of the phase corrected vector w=e^(−jφ) _(h) v_(N) _(t) (l_(N) _(t) )−e₁;

3) generating a Householder matrix ${P = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}};$

4) padding zeros and a one in the previous expanded matrix V as $\begin{bmatrix} 1 & 0 & \ldots & 0 \\ 0 & \quad & \quad & \quad \\ \vdots & \quad & V_{t} & \quad \\ 0 & \quad & \quad & \quad \end{bmatrix};$ and

5) multiplying the Householder matrix with the padded matrix to finish one expansion.

Since the vector steps through the vector codebook, the number of runs for each FOR loop is equal to the number of vectors in the corresponding vector codebook. The index l is the index for the matrix finally generated. It increases as 1, 2, . . . , L_(Nt)*L_(Nt−1) . . . *L_(Nt−Ns+1), where L_(t) is the number of vectors in the vector codebook of dimension t.

To reduce complexity and speed up computation, the phase of the first entry of each vector may be removed when CGA 108 stores each vector codebook as e^(−jφ) _(h) v_(N) _(t) (l_(N) _(t) ). Namely, each first element of each vector in each vector codebook is real (not complex). The real number $\frac{2}{{w^{H}w}}$ may also be pre-computed and stored for each vector. Technique 2

According to one embodiment, CGA 108 may well implement a second technique to generate one or more matrix codeword(s) from vector codebook(s) for 2-, 3-, 4-, . . . N-unit vectors. In this technique, CGA 108 employs the complementary property of unitary matrix as follows. Instead of generating a N_(t) by N_(s) matrix directly, it first generates a N_(t) by N_(s) matrix first and then cut a submatrix of dimension N_(t) by N_(s) from it.

This technique is the most efficient when it generates matrix codebook of dimension N_(t) by (N_(t)−1). As specified above, the stored N_(t)-vector codebook has the property that the N_(t)-vector codewords are spread over the complex N_(t)-sphere as uniformly as possible, where the minimum angle between any two vectors is maximized. Note that each vector has a complementary, orthogonal subspace, spanned by (N_(t)−1) orthogonal vectors, and is orthogonal to the vector. The property of the vector codebook implies that the subspaces (i.e. N, by (N_(t)−1) matrixes) are uniformly spread, where the minimum angle between any two subspaces is maximized. This maximum minimum angle is a desirable property for N_(t) by (N_(t)−1) matrix codebook. The major advantage of scheme 2 is that only one vector codebook is required to generate the N_(t) by (N_(t)−1) codebook, while (N_(t)−1) vector codebooks are required in Technique 1.

Technique 2 is also efficient to generate matrix codebook of dimension N_(t) by N_(s), where $N_{s} > {\frac{N_{t}}{2}.}$ For this case, the CGA 108 first generates an N_(t)×N_(s) matrix, and then cuts a N_(t) by N_(s) submatrix from it as a matrix codeword. The pseudo code of the scheme is as follows, where the notations are already defined, above, in Technique 1. It is assumed that $N_{s} > {\frac{N_{t}}{2}.}$

One advantage of this scheme is that only N_(t)−N_(s) vector codebooks are required to generate the N_(t)×N_(s) codebook, while N_(s) vector codebooks may be required for Technique 1. I.  FOR  l_(N_(s) + 1) = 1 : L_(N_(s) + 1) I.1.  w = e^(−j  ϕ_(l))v_(N_(s) + 1)(l_(N_(s) + 1)) − e₁ $\begin{matrix} {{where}\quad\phi_{1}\quad{is}\quad{the}\quad{phase}\quad{of}\quad{the}\quad{first}} \\ {{{entry}\quad{of}\quad{v_{N_{t} - N_{s} + 2}\left( l_{N_{t} - N_{s} + 2} \right)}\quad{and}\quad e_{1}} = {\left\lbrack {1,{0\quad\cdots\quad 0}} \right\rbrack^{T}.}} \end{matrix}\quad$ ${I{{.2}.\quad V_{t}}} = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}$ II.  FOR  l_(N_(s) + 2) = 1 : L_(N_(s) + 2) I  I.1.  w = e^(−j  ϕ_(l))v_(N_(s) + 2)(l_(N_(s) + 2)) − e₁, $\begin{matrix} {{where}\quad\phi_{1}\quad{is}\quad{the}\quad{phase}\quad{of}\quad{the}\quad{first}} \\ {{{entry}\quad{of}\quad{v_{N_{s} + 2}\left( l_{N_{s} + 2} \right)}\quad{and}\quad e_{1}} = {\left\lbrack {1,{0\quad\cdots\quad 0}} \right\rbrack^{T}.}} \end{matrix}\quad$ ${{II}{{.2}.\quad P_{N_{s} + 2}}} = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}$ ${{II}{{.3}.\quad V_{t}}} = {P_{N_{s} + 2}\begin{bmatrix} 1 & 0 \\ 0 & V_{t} \end{bmatrix}}$ III.  FOR  l_(N_(s) + 3) = 1 : L_(N_(s) + 3) . . .    . . . N_(s).  FOR  l_(N_(t)) = 1 : L_(N_(t)) N_(s).1.  w = e^(−j  ϕ_(l))v_(N_(t))(l_(N_(t))) − e₁, where  ϕ₁  is  the  phase  of  the  first  entry  of  v_(N_(t))(l_(N_(t))). ${N_{s}{{.2}.\quad P_{N_{t}}}} = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}$ ${N_{s}{{.3}.\quad V_{t}}} = {{P_{N_{t}}\quad\begin{bmatrix} 1 & 0 & \cdots & 0 \\ 0 & \quad & \quad & \quad \\ \vdots & \quad & V_{t} & \quad \\ 0 & \quad & \quad & \quad \end{bmatrix}}.}$ N_(s).4. V(l) = last N_(s) columns of V_(t), ${{where}\quad l} = {l_{N_{t}} + {\sum\limits_{i = {N_{s} + 1}}^{N_{t} - 1}{l_{i}{\prod\limits_{j = {i + 1}}^{N_{t}}\quad L_{j}}}}}$ N_(s).5. END . . .    . . . II.4. END I.3. END Technique 3

According to one embodiment, CGA 108 may well implement a third technique to generate one or more matrix codeword(s) from vector codebook(s) for 2-, 3-, 4-, . . . N-unit vectors. This technique represents a further simplification over technique 2, above. For the generation of an N_(t) by (N_(t)−1) codebook, techniques 2 and 3 are very similar in terms of computational complexity. However, when generating an N_(t) by N_(s) codebook with L codewords, this third technique provides for the use of only one vector codebook with L codewords and span each vector into a N_(t) by N_(t) matrix using Householder reflection. The N_(t) by N_(s) matrix codebook is formed by taking a N_(t) by N_(s) submatrix from each spanned N_(t) by N_(t) matrix. Example pseudo code of for technique 3 is as follows: 1. FOR l = 1:L 2.  w = e^(−j  ϕ_(l))v_(N_(t))(l) − e₁ $\begin{matrix} {{where}\quad\phi_{1}\quad{is}\quad{the}\quad{phase}\quad{of}\quad{the}\quad{first}\quad{entry}\quad{of}\quad{v_{N_{t}}(l)}\quad{and}} \\ {e_{1} = {\left\lbrack {1,{0\quad\cdots\quad 0}} \right\rbrack^{T}.}} \end{matrix}\quad$ ${3.\quad V_{t}} = {I - {\frac{2}{{w^{H}w}}{ww}^{H}}}$ 4.  V(l) = last N_(s) columns of V_(t) 5. END Technique 4

According to one embodiment, CGA 108 may employ yet a fourth technique to generate matrix codeword(s) from vector codebook(s) for 2, 3, 4 . . . N-unit vectors, according to one embodiment. According to one embodiment, the fourth technique represents a further simplification of technique three, above. In particular, in step 4 of technique 3, CGA 108 may take any N_(s) columns of Vt, such as the first N_(s) columns, or N_(s) columns extracted for the N_(t) columns.

It should be appreciated that combinations of techniques 1-4 are possible, without deviating from the scope and spirit of the invention. For example, techniques 1 and 2 expand the matrix from a small core to a big matrix iteratively as shown above. According to one embodiment, the small core (or, lowest dimension) may be generated using other techniques. For example, to generate a 4×3 matrix, the core (lowest dimension) used in technique 1 may be generated by technique 3. In this regard, CGA 108 may use technique 3 to generate a core matrix of size 3×2 using a 3-vector codebooks, and then use the remaining teachings of technique 1 to finish the generation of 4×3 using the 3×2 core as the lowest dimension.

To illustrate the algorithm, we use an example of 3/6 bits vector codebooks to generate all the necessary matrix codebooks. In 802.16e, it may be desirable to implement codebooks, whose size L=3n bits, where n is integer.

Since there are many combinations of N_(t), N_(s), and L and each of them requires a corresponding codebook, the storage of all codebooks are burdensome. A set of codebooks is proposed, which can be dynamically generated with low complexity.

For small size codebooks, i.e. 2×1, 3×1, and 4×1 with 3 bit index, three optimized, random codebooks are stored. For 3×1 and 4×1 with 6 bit index, two structured codebooks are proposed, which can be dynamically generated using an improved Hochwald method. For all the other matrix codebooks such as 3×2 and 4×2, structured codebooks are proposed, which can also be dynamically generated with low complexity.

An example of stored vector codebooks for 2×1, 3×1, 4×1 with 3 bit index, and 2×1 with 6 bit index are listed below in Table 1, Table 2, Table 3, and Table 4. The notation v(N_(t), N_(s), L) denotes the matrix codebook (i.e. the set of complex unitary matrixes), which consists of 2^(L) unit matrixes of a dimension N_(t) by N_(s). The number L is the number of bits required for the feedback index that can indicate any vector in the codebook. TABLE 1 v(2, 1, 3) Vector index 1 2 3 4 5 6 7 8 ν₁ 1 0.794 0.794 0.794 0.794 0.329 0.511 0.329 ν₂ 0 −0.580 + 0.182i 0.058 + 0.605i −0.298 − 0.530i 0.604 + 0.069i 0.661 + 0.674i 0.475 − 0.716i −0.878 − 0.348i

TABLE 2 v(3, 1, 3) Vector index 1 2 3 4 5 6 7 8 ν₁ 1 0.500 0.500 0.500 0.500 0.495 0.500 0.500 ν₂ 0 −0.720 − 0.313i −0.066 + 0.137i −0.006 + 0.653i   0.717 + 0.320i 0.482 − 0.452i 0.069 − 0.139i −0.005 − 0.654i ν₃ 0   0.248 − 0.268i −0.628 − 0.576i   0.462 − 0.332i −0.253 + 0.263i 0.296 − 0.480i 0.620 + 0.585i −0.457 + 0.337i

TABLE 3 v(4, 1, 3) Vector index 1 2 3 4 5 6 7 8 ν₁ 1 0.378 0.378 0.378 0.378 0.378 0.378 0.378 ν₂ 0 −0.270 − 0.567i   −0.710 + 0.133i 0.283 − 0.094i −0.084 + 0.648i 0.525 + 0.353i 0.206 − 0.137i   0.062 − 0.333i ν₃ 0 0.596 + 0.158i −0.235 − 0.147i 0.070 − 0.826i   0.018 + 0.049i 0.412 + 0.183i −0.521 + 0.083i   −0.346 + 0.503i ν₄ 0 0.159 − 0.241i   0.137 + 0.489i −0.280 + 0.049i   −0.327 − 0.566i 0.264 + 0.430i 0.614 − 0.375i −0.570 + 0.211i

TABLE 4 v(2, 1, 6) Vector index 1 2 3 4 5 6 7 8 ν₁ 1 0.9744 0.9743 0.9743 0.9741 0.9739 0.9321 0.9320 ν₂ 0 0.2035 − j0.0961 −0.2250 − j0.0050 −0.0621 + j0.2166 0.1822 + j0.1340 0.0022 − j0.2268 −0.2925 + j0.2136 −0.2243 − j0.2847

TABLE 5 v(2, 1, 6) (cont'd) Vector index 9 10 11 12 13 14 15 16 ν₁ 0.9208 0.9207 0.9127 0.9048 0.8992 0.8972 0.8694 0.8629 ν₂ 0.3890 + j0.0303 0.2238 − j0.3196 0.2039 + j0.3542 −0.4083 − j0.1212 −0.0783 + 0.0093 − 0.4479 − 0.4307 + j0.4305 j0.4416 j0.2085 j0.2645

TABLE 6 v(2, 1, 6) (cont'd) Vector index 17 18 19 20 21 22 23 24 ν₁ 0.8603 0.8436 0.8361 0.8221 0.8218 0.8160 0.8094 0.7886 ν₂ −0.4974 + j0.1120 −0.3229 + j0.4291 −0.2299 − j0.4980 0.1186 + −0.4533 − 0.2462 − 0.5844 + −0.6044 − j0.5569 j0.3452 j0.5229 j0.0586 j0.1135

TABLE 7 v(2, 1, 6) (cont'd) Vector index 25 26 27 28 29 30 31 32 ν₁ 0.7757 0.7741 0.7737 0.7618 0.7556 0.7252 0.7194 0.6907 ν₂ 0.3859 + j0.4993 −0.0058 − j0.6330 −0.1463 + j0.6164 −0.5536 + 0.4976 − 0.6112 + 0.6705 − −0.4194 + j0.3364 j0.4259 j0.3170 j0.1815 j0.5891

TABLE 8 v(2, 1, 6) (cont'd) Vector index 33 34 35 36 37 38 39 40 ν₁ 0.6842 0.6828 0.6762 0.6744 0.6657 0.6343 0.6156 0.6129 ν₂ −0.2715 − j0.6769 −0.7221 + j0.1111 0.2196 + j0.7032 −0.5482 − 0.3454 − −0.7415 − 0.5315 + 0.0320 − j0.4946 j0.6615 j0.2187 j0.5819 j0.7895

TABLE 9 v(2, 1, 6) (cont'd) Vector index 41 42 43 44 45 46 47 48 ν₁ 0.6128 0.5915 0.5837 0.5645 0.5466 0.5173 0.5119 0.5018 ν₂ −0.1037 + j0.7834 −0.6850 + j0.4254 0.6336 − j0.5078 0.7888 + 0.8211 − −0.4757 − −0.4493 + −0.8626 + j0.2432 j0.1643 j0.7114 j0.7322 j0.0643

TABLE 10 v(2, 1, 6) (cont'd) Vector index 49 50 51 52 53 54 55 56 ν₁ 0.4938 0.4780 0.4562 0.4281 0.4259 0.3921 0.3822 0.3761 ν₂ 0.2917 + j0.8192 0.3911 − j0.7865 −0.7982 − j0.3934 0.6905 + j0.5831 −0.0806 − −0.7794 + 0.7782 − 0.9220 + j0.9012 j0.4887 j0.4983 j0.0917

TABLE 11 v(2, 1, 6) (cont'd) Vector index 57 58 59 60 61 62 63 64 ν₁ 0.3716 0.3080 0.2816 0.2568 0.2346 0.1951 0.1653 0.0866 ν₂ −0.1199 + −0.5759 − −0.9571 − 0.3374 − 0.4811 + −0.5888 + 0.9768 − −0.6811 − j0.9206 j0.7573 j0.0684 j0.9057 j0.8447 j0.7844 j0.1362 j0.7271

According to one embodiment, CGA 108 may generate matrix codebooks for multiple stream transmission from the vector codebooks in the previous section using three operations depicted below. We assume that all unit vectors in the section are complex with unit norm and the first entry of each vector is real. The first operation is called Householder reflection transformation. It is to generate a unitary N by N matrix H(v) using a unit N vector v as: $\begin{matrix} {{H\quad(v)} = \left\{ {\begin{matrix} {I,} & {v = e_{1}} \\ {{I - {pww}^{H}},} & {otherwise} \end{matrix},} \right.} & (2) \end{matrix}$ where w=v−e, and ${e_{1} = \left\lbrack {1\quad 0\quad\ldots\quad 0} \right\rbrack^{T}};{p = \frac{2}{{w^{H}w}}}$ and it is a real number that can be pre-computed and stored for each vector in the tables; I is the N by N identity matrix; ^(H) denotes the conjugate transpose operation.

The other two operations are built on Householder transformation. One of them is called H-concatenation, and the other is called H-expansion, where the “H” stands for Householder. The H-concatenation (HC) generates a N by M+1 unitary matrix from a unit N vector and a unitary N−1 by M matrix using Householder transformation as $\begin{matrix} {{{{HC}\left( {v_{N},A_{{({N - 1})} \times M}} \right)} = {{H\left( v_{N} \right)}\begin{bmatrix} 1 & 0 & \cdots & 0 \\ 0 & \quad & \quad & \quad \\ \vdots & A_{{({N - 1})} \times M} & \quad & \quad \\ 0 & \quad & \quad & \quad \end{bmatrix}}},} & (3) \end{matrix}$ where N−1≧M ; the N−1 by M unitary matrix has property A^(H)A=I. Since both terms on the left are unitary the output of HC is a unitary matrix. The H-expansion (HE) generates a N by M matrix from a unit N vector, v_(N), by taking the last M columns of H(v) as HE(v _(N) , M)=H(v _(N))_(:,N−M+1:N),   (4)

CGA 108 may selectively employ one or more of the operations defined in (2), (3), and (4) to jointly generate matrix codebooks as follows. In table 5, use of the nomenclature L bit codebook is intended to represent that the codebook has 2^(L) matrixes, which requires an L bit feedback index. TABLE 5 Construction operations for ^(N) _(t) by ^(N) ₅ beamforming matrix with 3, 6, and 9 bit codebooks. N₅ N_(t) 2 3 4 2 antennas, H(v(2, 1, 3)) 3 bit codebook 3 antennas, HE(v(3, 1, 3), 2) H(v(3, 1, 3)) 3 bit codebook 4 antennas, HE(v(4, 1, 3), 2) HE(v(4, 1, 3), 3) H(v(4, 1, 3)) 3 bit codebook 2 antennas, H(v(2, 1, 6)) 6 bit codebook 3 antennas, HC(v(3, 1, 3), v(2, 1, 3)) HC(v(3, 1, 3), H(v(2, 1, 3))) 6 bit codebook 4 antennas, HC(v(4, 1, 3), v(3, 1, 3)) HE(v(4, 1, 6), 3) H(v(4, 1, 6)) 6 bit codebook 3 antennas, HC(v(3, 1, 6), v(2, 1, 3)) HC(v(3, 1, 6), H(v(2, 1, 3))) 9 bit codebook 4 antennas, HC(v(4, 1, 6), v(3, 1, 3)) HC(v(4, 1, 3), HC(v(3, 1, 3), v(2, 1, 3))) HC(v(4, 1, 3), HC(v(3, 1, 3), H(v(2, 1, 3)))) 9 bit codebook The set notation v(N_(t), N_(s), L) in the input parameter of the operations (i.e. H, HC, and HE) denotes that each matrix/vector in the codebook v(N_(t), N_(s), L) is sequentially taken as an input parameter to the operations. The feedback index is constructed by concatenating all the indexes s of the input argument vector codebooks in binary format. For example, the feedback index of HC(v(4,1,6), v(3,1,3)) is constructed as i₂j₂, where i₂ and j₂ are the indexes of the vectors in codebook v(4,1,6) and v(3,1,3) in binary format respectively. Index Property:

According to one example embodiment, for the codebooks generated from HC operation, their indexes are formed by the concatenation of the indexes for the constituent codebooks. For example, the 4 by 2 6 bit codebook is formed by the concatenation of a 4 by 1 3 bit codebook and a 3 by 1 3 bit codebook. The 6 bit index of the 4 by 2 6 bit codebook can be divided into two portions. Namely, the first 3 bits are the index for the constituent 4 by 1 vector codebook, which determines the first column of the 4 by 2 beamforming matrix, and the last 3 bits are the index for the 3 by 1 vector codebook, which determines the second column of the beamforming matrix jointly with the first 3 bits.

The importance of the beamforming matrix columns usually decreases the column index because the column with a smaller column index corresponds to an eigenmode (or spatial channel) with a greater channel gain. The concatenation property of the index enables a scalable feedback. For example, we may feed back only the first few bits if there is not enough feedback bandwidth. For another example, we may assign different protections to the bits for different columns. For the third example, we may feed back the index for the first columns more often than those for the latter columns.

Reduction of Peak to Average Power Ratio (PAR)

The peak to average power ratio (PAR) is defined as $\begin{matrix} {{{PAR} = \frac{\max\limits_{t}{x_{i,t}}^{2}}{E_{t}\left\lbrack {x_{i,t}}^{2} \right\rbrack}},} & (5) \end{matrix}$ where X_(i,t) is the transmitted real signal on the i-th antenna at time instant t in time domain; the maximization and expectation are over time. It is desirable to reduce PAR to reduce the corresponding effect of transmission signal distortion in power amplifier. In developing the codebooks derived in, e.g., Tables 1-5, the effective PAR was not a design consideration and, in that regard, the resultant codebooks may not be designed to reduce (perhaps to a minimum), or limit the PAR. For example, codeword [1 0 0]^(T) is optimized for storage memory but not PAR reduction as all of the transmission power is allocated on the first power amplifier.

According to one example embodiment, CGA 108 may generate a codebook with improved PAR performance than those generated according to the techniques introduced above. In particular, CGA 108 may process one or more codeword matrix(es) associated with a codebook (e.g., such as one generated in accordance with the techniques introduced above) to generate a modified codeword matrix with improved PAR performance characteristics (e.g., reduced PAR) when compared to the original codeword matrix. According to one embodiment, CGA 108 may improve the effective PAR on a give codebook by applying one or more, e.g., two, unitary transformations Q_(C) and P_(C) to a given codebook as {tilde over (V)}=Q_(C)V P_(C),   (6) where V is the codeword matrix in codebook C of dimension N_(t) by N_(s); Q_(C) is a complex unitary matrix of dimension N_(t) by N_(t) and it is constant for the whole codebook C; P_(C) is a complex unitary matrix of dimension N_(s) by N_(s) and it is constant for the whole codebook C; {tilde over (V)} is the processed codeword matrix with improved PAR characteristics.

According to one example embodiment P_(C) is implemented as the identity matrix and, since the unitary transformation Q_(C) conducts a rotation on the whole codebook C, the rotated codebook {tilde over (C)} has the same structure (i.e. relative distances between codewords) as the original codebook C. Furthermore, for any unitary matrixes P_(C) and Q_(C), it can be shown that the distance between any two codewords V₁ and V₂ in codebook C is exactly the same as that between the corresponding codewords {tilde over (V)}₁ and {tilde over (V)}₂ in codebook {tilde over (C)}, where {tilde over (V)}1=Q_(C) V₁ P_(C) and {tilde over (V)}₂=Q_(C) V₂ P_(C). This implies that the transformed codebook {tilde over (C)} has the same structure as the original codebook C. In this regard, the PAR performance can be improved by identifying and utilizing unitary transformations Q_(C) and P_(C) that reduce the PAR (perhaps to a minimum). The numbers of the effective degrees of freedom of Q_(C) and P_(C) are N_(t) (N_(t)−1) and N_(s) (N_(s)−1) respectively.

In an OFDM system, for example, searching for appropriate Q_(C) and P_(C) to reduce, e.g., to a minimum, the PAR using the definition in equation (5) may be difficult, as the signal is in the time domain and the signal is the superposition of multiple sinusoids carrying data. Furthermore, the search results may vary with the OFDM parameters such as the number of subcarriers and the bandwidth. Accordingly, two criteria (perhaps sub-optimal) are derived for the searching, which focus the search for one subcarrier. The first criterion seeks to reduce (e.g., to a minimum) a maximum peak value, while the second criterion seeks to reduce (e.g., perhaps to a minimum) the mean power as shown in (7) and (8) respectively. $\begin{matrix} {{\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\frac{\max\limits_{{V \in C},i}\left( {\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}} \right)^{2}}{\underset{{V \in C},i}{E}{\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}^{2}}}}};} & (7) \\ {{\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\frac{\max\limits_{{V \in C},i}{\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}^{2}}}{\underset{{V \in C},i}{E}{\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}^{2}}}}};} & (8) \end{matrix}$ where {tilde over (V)}=Q V P; {tilde over (v)}_(i,j) denotes the entry of {tilde over (V)} on the i-th row and j-th column and its is the signal from one subcarrier among the superposition of all subcarriers; the maximization in the numerator and the expectation in the denominator are over the rows of {tilde over (V)} and all codeword {tilde over (V)} in the rotated codebook {tilde over (C)}. Since the denominator in (7) and (8) is a constant independent of Q and P, equation (7) and (8) can be rewritten as $\begin{matrix} {{\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\quad{\max\limits_{{V \in C},i}\left( {\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}} \right)^{2}}}},} & (9) \\ {\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\quad{\max\limits_{{V \in C},i}{\sum\limits_{j = 1}^{N_{s}}{{{\overset{\sim}{v}}_{i,j}}^{2}.}}}}} & (10) \end{matrix}$

For systems employing high order QAM such as 64 QAM, the transformation P_(C) provides little help and thus can be dropped. In this regard, expression (9) and (10) can be simplified as $\begin{matrix} {{Q_{C} = {\underset{Q}{argmin}\quad{\max\limits_{{V \in C},i}\left( {\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}} \right)^{2}}}},} & (11) \\ {{Q_{C} = {\underset{Q}{argmin}\quad{\max\limits_{{V \in C},i}{\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}^{2}}}}},} & (12) \end{matrix}$ where {tilde over (V)}=Q V. Performance Analysis

Turning briefly to FIG. 3, a graphical representation of the performance improvements achieved through use of the codebook generation agent is depicted, according to one embodiment of the invention. The proposed codebook generation techniques were simulated and compared to conventional techniques. The frequency permutation is Band AMC in 802.16e D5 standard. ITU Pedestrian B, LOS channel model with 0.2 transmit antenna correlation is employed. Perfect channel estimation and slow speed are assumed. The amount of feedback from the mobile device (e.g., 106) to the base station (e.g., 102) is 6 bits per AMC band. With reference to FIG. 3, the feedback is the codebook index pointing a matrix codeword in a 64-codeword codebook. Packet error rate (PER) at the downlink is simulated, where packet size is 1000 bytes. As shown in FIG. 3, the simulation results demonstrate that the proposed technique(s) 302 provide similar or better performance with much lower storage complexities than conventional technique(s) 304.

Having introduced the communication environment and operating characteristics of CGA 108 with respect to FIGS. 1 and 2, above, reference is now directed to FIG. 4 which provides an example electronic device architecture within which the CGA 108 may be practiced.

FIG. 4 illustrates a block diagram of an example architecture of an electronic device within which the teachings of the present invention may be practiced, according to one embodiment. Electronic device 400 includes antennas, physical layer (PHY) 402, media access control (MAC) layer 404, network interface(s) 406, processor(s) 408, and memory 410. In some embodiments, electronic device 400 may be a station capable of generating one or more matrix codebook(s) from matrix codeword(s) dynamically generated from vector codebook(s) for 2, 3, 4, . . . N-unit vectors by selectively performing Householder transformations as described above. In other embodiments, electronic device 400 may be a station that receives feedback index, and performs beamforming in a MIMO system. For example, electronic device 400 may be utilized in a wireless network as station 102 or station 104 (FIG. 1). Also for example, electronic device 400 may be a station capable of performing the calculations shown in any of the equations above.

In some embodiments, electronic device 400 may represent a system that includes an access point, a mobile station, a base station, or a subscriber unit as well as other circuits. For example, in some embodiments, electronic device 400 may be a computer, such as a personal computer, a workstation, or the like, that includes an access point or mobile station as a peripheral or as an integrated unit. Further, electronic device 400 may include a series of access points that are coupled together in a network.

In operation, device 400 may send and receive signals using one or more of the antennas, wherein the signals are processed by the various elements shown in FIG. 4. As used herein, the antennae may be an antenna array or any type of antenna structure that supports MIMO processing. Device 400 may operate in partial compliance with, or in complete compliance with, a wireless network standard such as, e.g., the 802.11 or 802.16 standards introduced above.

Physical layer (PHY) 402 is selectively coupled to one or more of the antennae to interact with a wireless network. PHY 402 may include circuitry to support the transmission and reception of radio frequency (RF) signals. For example, in some embodiments, PHY 402 may include an RF receiver to receive signals and perform “front end” processing such as low noise amplification (LNA), filtering, frequency conversion or the like. Further, in some embodiments, PHY 402 may include transform mechanisms and beamforming circuitry to support MIMO signal processing. Also for example, in some embodiments, PHY 402 may include circuits to support frequency up-conversion, and an RF transmitter.

Media access control (MAC) layer 404 may be any suitable media access control layer implementation. For example, MAC 404 may be implemented in software, or hardware or any combination thereof. In some embodiments, a portion of MAC 540 may be implemented in hardware, and a portion may be implemented in software that is executed by processor 408. Further, MAC 404 may include a processor separate from processor 408.

In operation, processor 408 may read instructions and data from memory 410 and perform actions in response thereto. For example, processor 408 may access instructions from memory 410 and perform method embodiments of the present invention, such as method 200 (FIG. 2) or other methods described herein. In this regard, processor 408 is intended to represent any type of processor, including but not limited to, a microprocessor, a digital signal processor, a microcontroller, or the like.

Memory 410 represents an article that includes a machine readable medium. For example, memory 410 represents a random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read only memory (ROM), flash memory, or any other type of article that includes a medium readable by processor 408. Memory 410 may store instructions for performing the execution of the various method embodiments of the present invention. Memory 410 may also store vector codebooks of 2, 3, 4, . . . N-unit vectors, although the invention is not limited in this respect.

Network interface 406 may provide communications between electronic device 400 and other systems. For example, in some embodiments, electronic device 400 may be an access point that utilizes network interface 406 to communicate with a wired network or to communicate with other access points. In some embodiments, electronic device 400 may be a network interface card (NIC) that communicates with a computer or network using a bus or other type of port.

As used herein, embodiments of CGA 108 may well be implemented in one or more of PHY 402, MAC 404, processor(s) 408, and/or combinations thereof. As introduced above, CGA 108 may well be implemented in hardware, software, firmware or combinations thereof.

Although the various elements of device 400 are depicted as disparate elements in FIG. 4, embodiments are envisioned that may combine one or more elements, or that may contain more elements. For example, the circuitry of processor 408, memory 410, network interface 406, and MAC 404 may well be integrated into a single integrated circuit. Alternatively, memory 410 may be an internal memory within processor 408, or may be a microprogram control store within processor 410. In some embodiments, the various elements of device 400 may be separately packaged and mounted on a common circuit board. In other embodiments, the various elements are separate integrated circuit dice packaged together, such as in a multi-chip module, and in still further embodiments, various elements are on the same integrated circuit die.

Alternate Embodiment(s)

FIG. 5 illustrates a block diagram of an example storage medium comprising content which, when invoked, may cause an accessing machine to implement one or more aspects of the codebook generation agent 108 and/or associated methods 300. In this regard, storage medium 500 may include content 502 (e.g., instructions, data, or any combination thereof) which, when executed, causes an accessing appliance to implement one or more aspects of the codebook generation agent 262 described above.

The machine-readable (storage) medium 500 may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnet or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing electronic instructions. Moreover, the present invention may also be downloaded as a computer program product, wherein the program may be transferred from a remote computer to a requesting computer by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem, radio or network connection). As used herein, all of such media is broadly considered storage media.

It should be understood that embodiments of the present invention may be used in a variety of applications. Although the present invention is not limited in this respect, the circuits disclosed herein may be used in many apparatuses such as in the transmitters and receivers of a radio system. Radio systems intended to be included within the scope of the present invention include, by way of example only, wireless local area networks (WLAN) devices and wireless wide area network (WWAN) devices including wireless network interface devices and network interface cards (NICs), base stations, access points (APs), gateways, bridges, hubs, cellular radiotelephone communication systems, satellite communication systems, two-way radio communication systems, one-way pagers, two-way pagers, personal communication systems (PCS), personal computers (PCs), personal digital assistants (PDAs), sensor networks, personal area networks (PANs) and the like, although the scope of the invention is not limited in this respect. Such devices may well be employed within any of a variety of

Embodiments of the present invention may also be included in integrated circuit blocks referred to as core memory, cache memory, or other types of memory that store electronic instructions to be executed by the microprocessor or store data that may be used in arithmetic operations. In general, an embodiment using multistage domino logic in accordance with the claimed subject matter may provide a benefit to microprocessors, and in particular, may be incorporated into an address decoder for a memory device. Note that the embodiments may be integrated into radio systems or hand-held portable devices, especially when devices depend on reduced power consumption. Thus, laptop computers, cellular radiotelephone communication systems, two-way radio communication systems, one-way pagers, two-way pagers, personal communication systems (PCS), personal digital assistants (PDA's), cameras and other products are intended to be included within the scope of the present invention.

The present invention includes various operations. The operations of the present invention may be performed by hardware components, or may be embodied in machine-executable content (e.g., instructions), which may be used to cause a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the operations. Alternatively, the operations may be performed by a combination of hardware and software. Moreover, although the invention has been described in the context of a computing appliance, those skilled in the art will appreciate that such functionality may well be embodied in any of number of alternate embodiments such as, for example, integrated within a communication appliance (e.g., a cellular telephone).

In the description above, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details. In other instances, well-known structures and devices are shown in block diagram form. Any number of variations of the inventive concept are anticipated within the scope and spirit of the present invention. In this regard, the particular illustrated example embodiments are not provided to limit the invention but merely to illustrate it. Thus, the scope of the present invention is not to be determined by the specific examples provided above but only by the plain language of the following claims. 

1. A method comprising: processing each codeword matrix associated with a codebook to generate a modified codebook with an improved peak to average power ratio (PAR) as compared to the codebook.
 2. A method according to claim 1, the element of processing comprising: applying at least one of two unitary transformations Q_(C) and P_(C) to a given codebook according to {tilde over (V)}=Q_(C) V P_(C), where V is the codeword matrix in codebook C of dimension N_(t) by N_(s), Q_(C) and P_(C) are complex unitary matrices of dimensions N_(t) by N_(t) and N_(s) by N_(s) respectively and are constant for an entirety of codebook C.
 3. A method according to claim 2, wherein one or more of Q_(C) and P_(C) are derived according to at least one of $\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\quad{\max\limits_{{V \in C},i}{\left( {\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}} \right)^{2}\quad{and}}}}$ $\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\quad{\max\limits_{{V \in C},i}{\sum\limits_{j = 1}^{N_{s}}{{{\overset{\sim}{v}}_{i,j}}^{2}.}}}}$
 4. A transceiver comprising: a memory including data representing one or more codebook(s) of one or more codeword matrix(es); and a codebook generation agent, coupled to the memory, to implement a method according to claim
 1. 5. A receiver comprising: a codebook generation agent, to process each codeword matrix associated with a codebook to generate a modified codebook with an improved peak to average power ratio (PAR) as compared to the original codebook.
 6. A receiver according to claim 5, wherein the codebook generation agent selectively applies at least one of two unitary transformations Q_(C) and P_(C) to a given codebook according to {tilde over (V)}=Q_(C) V P_(C), where V is the codeword matrix in codebook C of dimension N_(t) by N_(s), Q_(C) and P_(C) are complex unitary matrices of dimensions N_(t) by N_(t) and N_(s) by N_(s) respectively and are constant for an entirety of codebook C.
 7. A receiver according to claim 6, wherein the codebook generation agent selectively derives at least one of Q_(C) and P_(C) according to at least one of $\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\quad{\max\limits_{{V \in C},i}{\left( {\sum\limits_{j = 1}^{N_{s}}{{\overset{\sim}{v}}_{i,j}}} \right)^{2}\quad{and}}}}$ $\left\{ {Q_{C},P_{C}} \right\} = {\underset{Q,P}{argmin}\quad{\max\limits_{{V \in C},i}{\sum\limits_{j = 1}^{N_{s}}{{{\overset{\sim}{v}}_{i,j}}^{2}.}}}}$
 8. A storage medium comprising a modified codebook, the modified codebook generated through a process applying at least one complex unitary transform to a select codeword matrix from the codebook.
 9. A storage medium according to claim 8, wherein the modified codebook is derived by applying each codeword matrix of an original codebook to one or more of complex unitary matrices Q_(C) and P_(C), which generate the modified codebook with a same distance structure as the original codebook and improved PAR characteristics. 